Boni Maxime Ale, MD, MSc, MPH’s Post

A BIG update to the {gtsummary} R package is coming in a couple of weeks! 🎉 Please check out the release notes here: https://lnkd.in/grXurM3S I would be super appreciative if you could take the new version for a spin before we send it off to CRAN. For my pharma friends out there, this version of the package is compatible with the emerging Analysis Result Dataset (ARD) standard from CDISC. Stay tuned for even more pharma+ARD updates!

This project focuses on the analysis of drug-related data to gain insights into drug classes, medical conditions, side effects, and other relevant parameters using data visualization and management tools. The dataset used contains information about various drugs, including columns such as Drug Name, Drug Class, Medical Condition, Dosage Form, Route of Administration, Side Effects, Manufacturer, and Price. The backend data is stored and managed using MySQL, while Apache NetBeans is used to develop a user-friendly application for data entry and management. The frontend visualizations are created using Tableau, where multiple graphs and charts represent key insights, including the top 10 drugs by price, the most common side effects, and the distribution of drug classes. This project aims to provide a comprehensive analysis of drug data to help understand drug classifications, their associated conditions, and their pricing, as well as to identify patterns and trends in side effects.The successful integration of database management with dynamic visualizations highlights the importance of data analysis in the healthcare sector, providing a solid framework for future decision-making and research on drug efficacy and safety.

Something that, I think, will be of interest to those following the TransCelerate BioPharma Inc. and CDISC Digitial Data Flow (DDF) and the Unified Study Definitions Model (USDM) work. A few weeks back there was an excellent "DDF in Action" day held by TransCelerate and hosted jointly at J&J in New Jersey and at Novo Nordisk in Copenhagen. I was asked to do the usual "introduction to USDM" talk and just fancied doing something a little different, so I did a single "infographic" / slide. The slide starts to paint a picture of all the use cases where a "digital protocol" might impact the development lifecycle. If this was a jigsaw puzzle, this is the corners and the edge pieces, the obvious use cases. There are many more, the great bulk of the picture in the middle of the jigsaw, the list is not exhaustive by any means. The good thing, it might be a bad thing, we are not sure what picture will emerge when we complete the jigsaw. However, we have the opportunity to shape that picture. One thing I pointed out when presenting this is we, as an industry, love putting things into buckets, we love to categorise stuff. Across the top in blue are what most people consider the main areas in the lifecycle. But I noted during the talk that this will become much more of a continuum, multiple roles adding increasing detail to a study definition that incorporates the protocol (and allow for it to be rendered) but will also provide so much more. What would be wonderful is if the community could give me more use cases ... nothing like a bit of crowd sourcing. Just add any to the comments below. One comment I often make is that there is a use case any time we pick up that protocol PDF and transcribe some information from it, dare I say it, into Excel 😀 One note, the retrospective versus prospective bar across the top. Retrospective is digitising old protocols to inform the writing of new protocols. Prospective is the development of a new protocol and its subsequent use within a study. Any comments / thoughts most welcome.

Interested to learn more about Digital Data Flow (DDF)? You're in luck! This infographic is a great summary of the variety of clinical development use cases that Faro is working toward addressing through seamless, automated data flow in line with TransCelerate BioPharma Inc.'s Digital Data Flow initiative. Here at Faro, we're especially focused on the first two categories. Reach out to us to learn more about our approach, or for a demo of Study Designer! (Thanks David Iberson-Hurst - Partner at d4k! for putting this together!) #clinicaldevelopment #clinicaloperations #digitaldataflow #clinicaldata #clinicalAI

Struggling to utilize valuable historical clinical trial data? Traditional formats can hinder analysis. CDISC (Clinical Data Interchange Standards Consortium) conversions hold the key! By converting your legacy data to CDISC standards, you can: - Boost Collaboration: Share data seamlessly across studies and institutions. - Unlock New Insights: Facilitate secondary analysis and data mining for hidden gems. - Stay Regulatory Compliant: Meet evolving requirements for standardized data. Considering a CDISC conversion project? Partner with us and ensure compliance. https://lnkd.in/gR7tSxwV

📊 Are you aiming to become a proficient Data Engineer? If so, mastering SQL is essential! 🚀 🔹 Scenario : Pharma Health Corporation wants to identify its top 3 most profitable medications sold in its pharmacies, each sourced from a unique supplier. Your task is to write a SQL query that retrieves these top 3 medications along with their profits, sorted from highest to lowest total profit. Given : Table: pharmacy_sales Columns: - product_id (integer) - units_sold (integer) - total_sales (decimal) - cogs (decimal) - manufacturer (varchar) - drug (varchar) Total Profit = Total Sales - Cost of Goods Sold (COGS) #DataEngineering #SQLChallenge #DataAnalysis #LearningJourney #ProblemSolving #SQLQueries

Boost your pharmacovigilance efficiency with AI-powered platform! Experience faster literature screening, extensive coverage, tailored searches, and seamless reporting. #GenerativeAI #HealthTech #Pharmacovigilance #AI #FutureIntelligence #DataProcessing

Make a woxl for pharma SOP to word and get data auto computed Use woxl to create standard operating procedure in a simple word document with proper tags and the cross tag formula, and the data when entered will work like an excel and at the end, the data sheet can be downloaded.

Top FAQs for Clinical ADaM Programmer (Safety & Efficacy Domains). What is ADaM, and why is it important? ADaM (Analysis Data Model) is part of CDISC standards, designed to facilitate clear and efficient analysis of clinical trial data for regulatory submissions, focusing on both safety and efficacy. What are the key components of ADaM datasets in safety domains? Key components include adverse events (AE), exposure (EX), and laboratory data (LB), focusing on patient safety signals and side effect tracking. What do ADaM efficacy datasets typically include? Efficacy datasets focus on primary and secondary endpoints, including response variables, progression, and survival metrics in clinical trials. How do ADaM datasets support statistical analysis? They are structured to be analysis-ready, making it easier for biostatisticians to run models and generate tables, listings, and figures (TLFs) for submission. What are some common ADaM dataset challenges? Maintaining compliance with ADaM specifications while handling complex trial data, and ensuring traceability from raw data to analysis datasets. How does ADaM ensure data consistency across studies? ADaM standardizes data structures, variable names, and formats, enabling consistency and comparability across studies for regulatory reviews. #letstalktech

Data management and analysis are the backbone of clinical research in the pharmaceutical and biotechnology industries. Effective data management ensures accuracy, regulatory compliance, and patient safety, while data analysis assesses the efficacy and safety of new treatments. Discover the key components, techniques, and challenges involved in managing and analyzing clinical trial data to advance medical science and improve healthcare outcomes. #ClinicalResearch #DataManagement #DataAnalysis #PharmaInnovation #BiotechResearch